Slide-Down Prevention for Wheeled Mobile Robots on Slopes

Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Model for Measuring Fair Labour Justice in Hotels: Design for the Spanish Case.

There is a growing awareness of Corporate Social Responsibility (CSR) and sustainability as a global movement. The hospitality sector is one of the major industries driving socioeconomic development worldwide (especially in economies such as Spain) and it has responded to this need, in the context of a general worsening of labor conditions in this sector. Evidence of this response is the Fair Hotels Project, which is an international collaborative effort aimed at building new partnerships between fair trade movements and trade unions in order to have a positive effect on the labour market in the hotel sector. This article describes the design of Hoteles Justos Laboralmente Responsables (HJLR), a fair labour justice and socially responsible model for hotels oriented to contribute to sustainability and labour justice within the Spanish hotel sector. The HJLR model was created to meet corporate, labour and local development needs. It includes accurate and objective measures—and homogeneous and comparable indicators—to assess the level of fairness and quality of labour practices of hotels. This model would be of great utility in improving the sustainability and quality of life of people working in this economic sector and could be also used by companies to improve their competitive position. The Spanish Government has shown its support for this project as a part of its 2030 sustainable tourism strategy, aimed to get the United Nation Sustainable Development Goals. Furthermore, this is a relevant line for future research, once the implementation phase is completed and quantitative data is available to measure the situation in depth

REDPETUR: collaborative experience

REDPETUR is a competency-based teaching network in the field of internships in tourism studies. Its goal is to successfully address the role of these studies in society and optimize the public resources used. This Network has carried out a series of activities, thanks to the financing of the Integral Teaching Plan of the University of Malaga, which have contributed to the adaptation of training in pandemic times using information technologies (IT) such as Virtual Campus and Icaro platform (an ecosystem which connects companies, students and teachers). In addition, from a Delphi analysis this network has set the foundations for collaboration between tourism faculties sharing generated content to ensure their students gain practical experience to complete their education.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Methods for autonomous wristband placement with a search-and-rescue aerial manipulator

A new robotic system for Search And Rescue (SAR) operations based on the automatic wristband placement on the victims’ arm, which may provide identification, beaconing and remote sensor readings for continuous health monitoring. This paper focuses on the development of the automatic target localization and the device placement using an unmanned aerial manipulator. The automatic wrist detection and localization system uses an RGB-D camera and a convolutional neural network based on the region faster method (Faster R-CNN). A lightweight parallel delta manipulator with a large workspace has been built, and a new design of a wristband in the form of a passive detachable gripper, is presented, which under contact, automatically attaches to the human, while disengages from the manipulator. A new trajectory planning method has been used to minimize the torques caused by the external forces during contact, which cause attitude perturbations. Experiments have been done to evaluate the machine learning method for detection and location, and for the assessment of the performance of the trajectory planning method. The results show how the VGG-16 neural network provides a detection accuracy of 67.99%. Moreover, simulation experiments have been done to show that the new trajectories minimize the perturbations to the aerial platform.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes.

This paper evaluates four semantic segmentation models in Search-and-Rescue (SAR) scenarios obtained from ground vehicles. Two base models are used (U-Net and PSPNet) to compare different approaches to semantic segmentation, such as skip connections between encoder and decoder stages and using a pooling pyramid module. The best base model is modified by including two attention mechanisms to analyze their performance and computational cost. We conduct a quantitative and qualitative evaluation using our SAR dataset defining eleven classes in disaster scenarios. The results demonstrate that the attention mechanisms increase model performance while minimally affecting the computation time.This work has been partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project PID2021-122944OB-I00. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis of Tread ICRs for Wheeled Skid-Steer Vehicles on Inclined Terrain

The instantaneous centers of rotation (ICRs) for the two treads of skid-steer vehicles moving with low inertia on hard horizontal terrain almost remain with constant local coordinates, which allows to establish an equivalence with differential-drive locomotion. However, this significant kinematic relationship has not been analyzed yet on sloped ground. One relevant difficulty of studying ICR behavior on inclined terrain, even on a flat surface, is the continuous variation of pitch and roll angles while turning. To overcome this problem, this paper analyzes a dynamic simulation of a skid-steer vehicle on horizontal ground where gravity is substituted by an equivalent external force in such a way that pitch and roll are kept constant. Relevant tread ICR variations on inclined ground have been deduced, which have a significant impact on skid-steer kinematics. These new findings have been corroborated experimentally with a four-wheeled mobile robot that turns on an inclined plane.Spanish Project PID2021-122944OB-I0

CNN-Based Methods for Object Recognition With High-Resolution Tactile Sensors.

Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as convolutional neural networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic end-effector to identify contacted objects. Two CNN-based approaches have been employed to classify pressure images. These methods include a transfer learning approach using a pre-trained CNN on an RGB-images dataset and a custom-made CNN (TactNet) trained from scratch with tactile information. The transfer learning approach can be carried out by retraining the classification layers of the network or replacing these layers with an SVM. Overall, 11 configurations based on these methods have been tested: eight transfer learning-based, and three TactNet-based. Moreover, a study of the performance of the methods and a comparative discussion with the current state-of-the-art on tactile object recognition is presented

Identificación de parámetros borrosos para el control de suspensión activa mediante enjambre de partículas

[Resumen] Este artículo aborda la identificación de parámetros borrosos mediante técnicas de optimización de enjambre de partículas (PSO) y su aplicación al control de un sistema de suspensión activa. En particular, se adopta un controlador de tipo Takagi-Sugeno de orden cero con Partición Difusa Estándar de sus antecedentes. A diferencia de trabajos previos, donde el aprendizaje se limitaba a los parámetros de escala del control, el método propuesto permite la optimización de los conjuntos borrosos de los antecedentes. La metodología propuesta se ha experimentado con éxito sobre un sistema físico de un cuarto de vehículo.Este trabajo ha sido financiado parcialmente por los proyectos CICYT DPI 2011-22443 and DPI2015-65186-Rhttps://doi.org/10.17979/spudc.978849749808

Disaster area recognition from aerial images with complex-shape class detection.

This paper presents a convolutional neural network (CNN) model for event detection from Unmanned Aerial Vehicles (UAV) in disaster environments. The model leverages the YOLOv5 network, specifically adapted for aerial images and optimized for detecting Search and Rescue (SAR) related classes for disaster area recognition. These SAR-related classes are person, vehicle, debris, fire, smoke, and flooded areas. Among these, the latter four classes lead to unique challenges due to their lack of discernible edges and/or shapes in aerial imagery, making their accurate detection and performance evaluation metrics particularly intricate. The methodology for the model training involves the adaptation of the pre-trained model for aerial images and its subsequent optimization for SAR scenarios. These stages have been conducted using public datasets, with the required image labeling in the case of SAR-related classes. An analysis of the obtained results demonstrates the model’s performance while discussing the intricacies related to complex-shape classes. The model and the SAR datasets are publicly available.This work has been partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project PID2021- 122944OB-I00. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech